Environmental Health

Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions.

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quant...

Long-term PM exposure and the clinical application of machine learning for predicting incident atrial fibrillation.

Clinical impact of fine particulate matter (PM) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification ...

Green Supplier Selection Using Fuzzy Multiple-Criteria Decision-Making Methods and Artificial Neural Networks.

In recent years, environmental awareness has increased considerably, and in order to decrease endangerments such as air and water pollution, and also ...

Ensemble-based deep learning for estimating PM over California with multisource big data including wildfire smoke.

INTRODUCTION: Estimating PM concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution healt...

Effectiveness of groundwater heavy metal pollution indices studies by deep-learning.

Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore...

Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET im...

Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) station...

Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization.

Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the a...

Ontological approach to the knowledge systematization of a toxic process and toxic course representation framework for early drug risk management.

Various types of drug toxicity can halt the development of a drug. Because drugs are xenobiotics, they inherently have the potential to cause injury. ...

Precision psychiatry in clinical practice.

The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance...

Detachment of the remora suckerfish disc: kinematics and a bio-inspired robotic model.

Remora suckerfish can attach to a wide diversity of marine hosts, however, their detachment mechanism remains poorly understood. Through analyzing hig...

A robust soft sensor to monitor 1,3-propanediol fermentation process by Clostridium butyricum based on artificial neural network.

With the aggravation of environmental pollution and energy crisis, the sustainable microbial fermentation process of converting glycerol to 1,3-propan...

Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes.

In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before pr...

The Silent Operation Theatre Optimisation System (SOTOS) to reduce noise pollution during da Vinci robot-assisted laparoscopic radical prostatectomy.

To reduce noise pollution and consequently stress during robot-assisted laparoscopic radical prostatectomy (RALP) the aim of our study was to evaluate...

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during ...

Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease.

The whole world is facing a great challenging time due to Coronavirus disease (COVID-19) caused by SARS-CoV-2. Globally, more than 14.6 M people have ...

Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective.

Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) ...

Optimal Selection of Sewage Treatment Technologies in Town Areas: A Coupled Multi-Criteria Decision-Making Model.

In recent years, the development of sewage treatment technologies has made many treatment options available in towns. Selecting the most appropriate a...

A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

BACKGROUND: Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensi...

Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture.

Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficienc...